I am an epidemiologist and data scientist with extensive experience in epidemiology, infectious diseases, maternal and neonatal health, cardiovascular diseases, cancer, nutrition, and machine learning applied to large-scale health datasets. PhD in Epidemiology at the University of São Paulo (USP) with a visiting research fellow at the London School of Hygiene & Tropical Medicine (LSHTM) in the UK. I hold a Master’s degree in Epidemiology from the Federal University of Bahia (UFBA), a Bachelor’s degree in Nutrition from Lúrio University, and a Postgraduate Diploma in Public Health with a focus on Monitoring, Evaluation, and Strategic Information (UFBA). In addition, I have completed an MBA in Data Science and Analytics (USP), another MBA in Artificial Intelligence and Big Data at the Institute of Mathematics and Computer Science - USP, and an MBA in Project Management at USP. I am currently pursuing a BSc in Economics at the Catholic University of Brazil.

Currently, I work as a Researcher in Health Data Science at the  School, contributing to a Wellcome Trust-funded project on predictive modelling for stillbirths and neonatal deaths across Sub-Saharan Africa. My role involves developing predictive models using classical statistical methods, machine learning algorithms, and AI techniques; managing and harmonising multi-country datasets (1 million birth records from more than 15 countries); and collaborating with ministries of health, international organisations, and academic institutions to generate evidence-based insights for global maternal and newborn health.
Before joining the school, I worked as a Data Scientist in a Technical Consultancy at PAHO/WHO (supporting Brazil’s Ministry of Health with COVID-19 surveillance and data analysis), as an epidemiologist and data scientist at the São Paulo State Health Department, and as a scientific curator and data analyst at Pacto Contra a Fome in Brazil. Earlier in my career, I worked as a Nutrition Program Manager at the Ministry of Health of Mozambique, leading district-level programmes and participating in national malnutrition assessments in collaboration with UNICEF and WFP.

I am also a member of several research networks, including LABDAPS-USP (Big Data and Predictive Analytics Laboratory in Health) and the Collaborative Scientific Network for COVID-19 generates evidence  (Rede-Covida in Brazil).

My research interests combine public health, epidemiology, and quantitative methods, with an emphasis on: descriptive and inferential statistics, survival analysis, time series approaches, predictive modelling, machine learning, and health data science.